气候变化对我国江南双季稻生产可能影响的数值模拟研究

在利用田间试验资料对双季稻生长动力（态）模拟模型进行验证的基础上,将基于GCMs的输出和历史气候资料相结合的气候变化情景与双季稻模式相连接,就气候变暖对我国江南双季稻主产区水稻生产的可能影响进行网格化定量模拟和客观评估,并就调整对策（改变播种日期和种植品种）在减缓气候变暖对双季稻生产影响中的作用作了初步的探讨。结果表明,在未来可能的气候变化情景下,若维持目前的品种和生产技术措施,双季稻产量将有不同程度的下降。产量变化的地域分布既有一定的规律性,又体现出气候变化影响的复杂性。适应对策分析表明,改种长生育期的     

气候变化对中国南方稻区水稻产量影响的模拟和分析

采用了DSSAT作物模式和区域气候模式相连接,模拟分析了A2和B2气候变化情景对中国主要地区灌溉水稻产量的影响。气候变化情景采用了IPCC发布的SRES(Special Report on Emissions Scenarios)系列的最新温室气体排放情景,气候情景值采用区域气候模式PRECIS(Provide Regional Climates for Impact Studies)的模拟值。通过研究站点水稻对A2和B2增温梯度敏感性的分析表明:温度增加,水稻产量呈下降趋势,随着温度增加,产量下降幅度增大。且在同一增温水平下,在南方热带地区的昆明和海口,产量下降幅度大于其他站点。A2和B2的产量相对于基准年(1961~1990年)的变化分别为:气候变化对不同站点的年代际水稻平均产量表现了正面或负面的影响(A2情景下为2.3%~-10.2%,B2情景下为4.0%~-13.6%),在某一些站点,水稻高产年和低产年的概率明显增加,产量分布趋于两极化。     

气候变率和气候变化对保加利亚作物产量的影响

近10年来,由人为因素与自然过程引起的气候波动与变化,已成为当今科技界最前沿的问题.本项目是研究保加利亚20世纪以来的气候变化,确认其对农业的全面影响.20世纪保加利亚年平均气温没有显著变化.但自20世纪70年代起,保加利亚夏半年的降水量有所下降.已建成保加利亚作物产量与降水、温度的多元统计回归模型.利用全球大气环流模型(GCM)的输出产品,创建了不同的渐变气候变化情景.利用农业技术决策支撑系统(DSSAT)3.5版估算气候变化对保加利亚玉米与小麦产量的影响.在目前CO2浓度(330PPM)不变的条件下,由于气温升高与降水减少的影响,作物生长季将缩短,GCM情景计算表明冬小麦及玉米产量下降,尤其是玉米产量下降.若考虑CO2浓度增加的直接效应,所有的GCM情景都反映出冬小麦将增产.为了减轻保加利亚未来气候变化对玉米产量的潜在影响,适应气候未来变化的对策可包括调整播期与选择适宜的杂交品种.     

利用随机天气模式及多种插值方法生成逐日气候变化情景的研究

针对目前大气环流模式在用于气候变化影响评估研究中时间分辨率较低的局恨性, 以及气候情景的要求和气候变化影响研究的需要, 结合GCM的模拟试验结果, 利用随机天气模式WGEN生成了中国东北地区未来气候变化的逐日情景, 其中包含了可能的气候变率信息, 可与作物动力模式等气候影响模式嵌套, 研究作物生长发育及其产量的可能变化, 及气候变率变化的可能影响等.     

我国双季早稻产量的变化规律和预报方法

本文根据我国双季稻主产区南方10省（市）双季早稻的产量和面积资料，分析了我国双季早稻生产的地区分布和产量变化规律。应用模糊聚类方法，将我国10个双季早稻生产省（市）分为四个区；根据谐波分析，认为我国双季早稻产量的变化有明显的准两年及其倍数周期；应用积分回归方法，分析了影响我国双季早稻生产的关键气象因子。我们在产量预报业务化试验期间，应用周期分析、环流模式、温度降水模式及专家系统等多种方法预报双季早稻产量，得到了较好的预报效果。     

A2和B2排放情景下气候变化对福建省水稻生产的阶段性影响

选择IPCC排放情景特别报告(SRES)中的A2和B2方案,利用区域气候模式PRECIS构建的气候变化情景文件与作物模型(CERES-Rice)耦合,采用雨养与灌溉两种方式,并综合考虑未来CO2浓度增加带来的直接增益效应,模拟了未来2020s及2040s两个时段气候变化对福建省水稻生育期与产量的影响。结果表明:无论是雨养方式还是灌溉方式,未来全省各稻区水稻生育期都将缩短,并且随着温度增高,2040s时段缩短的时间较2020s更长,单季稻生育期缩短时间最长,可达15~20 d。雨养条件下,除了闽东南双季稻区后季稻在2020s时段表现为2.3%(A2)和3.1%(B2)较小幅度的增产外,其他稻区各种稻作制度下的水稻产量较之BASE均出现了不同幅度的减产。闽西北稻区后季稻减产幅度最大,2020s时段A2和B2情景下减产幅度依次为6.9%和10.2%,2040s时段减产幅度进一步加大至14.1%和15.6%。闽东南稻区后季稻模拟结果较为乐观,尤其是在灌溉条件下表现为不同幅度的增产,两种情景下分别增产了1.7%、3.9%。双季稻种植区的后季稻产量稳定性均不如早稻和单季稻的,且随着温度升高,到2040s产量不稳定性有增加的趋势。灌溉在一定程度上可以缓解未来高温天气带来的产量波动。从全省的总产变化趋势来看,A2和B2两种排放情景模拟的结果都不容乐观,即使采用充分灌溉的方式,也依旧表现为减产。2020s时段,两种情景下分别减产0.74%与2.44%;2040s时段,两种情景下减产为3.50%与3.23%。未来早稻和单季稻生长季的土壤水分条件将变得不如目前湿润,与之相关的灌溉需要量均有所增加。     

未来气候变化对安徽淮河以南一季稻气候生产潜力的影响评估

针对未来气候变化及其对一季稻的可能影响,利用第5次耦合模式比较计划(coupled model intercomparison project phase 5,CMIP5)中5个气候模式(global circulation models,GCMs)和3种RCPs情景输出的逐日气候要素资料以及安徽淮河以南50个气象站1961—2010年逐日平均气温、降水量等观测资料和各县一季稻生育期、单产资料,预估未来21世纪安徽淮河以南一季稻生育期气候变化,并基于潜力衰减法估算近期(2018—2039年)、中期(2040—2069年)和远期(2070—2099年)一季稻气候生产潜力及其对气候变化的响应。结果表明:(1)5个GCMs对安徽淮河以南气温与降水量具有较好的模拟能力,且气温模拟效果更佳。(2)不同RCPs情景下未来一季稻各生育期将提前、全生育期缩短。预估的安徽淮河以南一季稻生育期持续增暖,北部增温幅度高于南部,其中RCP8.5情景下变暖幅度更显著;未来全生育期降水量整体变化趋势不明显,但南部增加较为明显,而太阳总辐射均显著减少。(3)不同RCPs情景预估的一季稻气候生产潜力均呈显著下降趋势,以远期降幅最大。(4)一季稻气候生产潜力与全生育期平均气温和降水量显著相关,且增暖负效应突出。可见,未来气候变化可能对一季稻气候生产潜力的提高不利。     

广西早稻产量农业气象预报模式探讨

广西地处亚热带,热量资源丰富,雨量充沛,全区的八个地区均可种植双季稻。但由于各个地区所处的地理位置不同,且地形复杂,气候条件有一定差异,因此各地区水稻的品种、种植季节、发育期不一,影响水稻产量的气候因子也不尽相同。本文根据早稻生育期内对各气象条件的要求,采用数理统计方法,建立各地区早稻产量农业气象预报模式,并根据各地区早稻产量对全区早稻产量的贡献大小,组成全区早稻产量预报模式。经1982、1983两年试报,效果较好。     

气候变化对小麦生产影响的数值模型研究

在未来气候变化对作物影响的研究基础上,分析未来不同气候情景对南京地区小麦生长发育、产量形成的影响,并考虑了紫外辐射变化的影响。采用数值模拟方法具体估算了温度升高、降水变化、ＣＯ２ 浓度上升及紫外辐射增强对南京地区小麦产量的影响。计算结果表明：未来ＣＯ２ 增加可提高小麦产量,气温升高、降水变化及紫外辐射增强均使得小麦产量有所降低。     

大气环流模式与春玉米生长模拟模式联接的影响评估模拟试验

根据春玉米田间试验资料和历史气候资料,对春玉米生长模拟模式进行了验证与灵敏性分析,在此基础上,运用逐步订正法将当前气候前景和大气环流模式输出资料结合历史气候资料生成的未来气候情景订正到１ｏ×１ｏ网格点上,与春玉米生长模拟模式相联接,就未来气候变化对我国东北地区春玉米生长、发育和最终产量的可能影响进行了网格化定量模拟,并对一些适应性对策的效果进行了定性或定量的分析。结果表明,在ＤＫＲＺＯＰＹＣ模拟的未来情景下,若保持当前作物品种和生产技术措施不变,研究区域除北部将平均增产７０％外,其余地区都将有不同程度的减产,幅度在－１０％～－５０％之间,而在ＮＣＡＲ模拟的情景下,中西部地区将增产,其它地区可维持当前产量水平。适应性对策将对开发利用未来可能的气候资源,减缓未来气候变化的负效应,充分发挥其正效应起到积极作用,进而绝大部分区域将受益于未来水热条件的改变。     

An assessment of regional vulnerability of rice to climate change in India

A simulation analysis was carried out using the InfoCrop-rice model to quantify impacts and adaptation gains, as well as to identify vulnerable regions for irrigated and rain fed rice cultivation in future climates in India. Climates in A1b, A2, B1 and B2 emission scenarios as per a global climate model (MIROC3.2.HI) and a regional climate model (PRECIS) were considered for the study. On an aggregated scale, the mean of all emission scenarios indicate that climate change is likely to reduce irrigated rice yields by ~4 % in 2020 (2010–2039), ~7 % in 2050 (2040–2069), and by ~10 % in 2080 (2070–2099) climate scenarios. On the other hand, rainfed rice yields in India are likely to be reduced by ~6 % in the 2020 scenario, but in the 2050 and 2080 scenarios they are projected to decrease only marginally (<2.5 %). However, spatial variations exist for the magnitude of the impact, with some regions likely to be affected more than others. Adaptation strategies comprising agronomical management can offset negative impacts in the near future—particularly in rainfed conditions—but in the longer run, developing suitable varieties coupled with improved and efficient crop husbandry will become essential. For irrigated rice crop, genotypic and agronomic improvements will become crucial; while for rainfed conditions, improved management and additional fertilizers will be needed. Basically climate change is likely to exhibit three types of impacts on rice crop: i) regions that are adversely affected by climate change can gain in net productivity with adaptation; ii) regions that are adversely affected will still remain vulnerable despite adaptation gains; and iii) rainfed regions (with currently low rainfall) that are likely to gain due to increase in rainfall can further benefit by adaptation. Regions falling in the vulnerable category even after suggested adaptation to climate change will require more intensive, specific and innovative adaptation options. The present analysis indicates the possibility of substantial improvement in yields with efficient utilization of inputs and adoption of improved varieties.     

Climate Change and Rice Yields in Diverse Agro-Environments of India. II. Effect of Uncertainties in Scenarios and Crop Models on Impact Assessment

Estimates of impact of climate change on crop production could be biased depending upon the uncertainties in climate change scenarios, region of study, crop models used for impact assessment and the level of management. This study reports the results of a study where the impact of various climate change scenarios has been assessed on grain yields of irrigated rice with two popular crop simulation models- Ceres-Rice and ORYZA1N at different levels of N management. The results showed that the direct effect of climate change on rice crops in different agroclimatic regions in India would always be positive irrespective of the various uncertainties. Rice yields increased between 1.0 and 16.8% in pessimistic scenarios of climate change depending upon the level of management and model used. These increases were between 3.5 and 33.8% in optimistic scenarios. At current as well as improved level of management, southern and western parts of India which currently have relatively lower temperatures compared to northern and eastern regions, are likely to show greater sensitivity in rice yields under climate change. The response to climate change is small at low N management compared to optimal management. The magnitude of this impact can be biased upto 32% depending on the uncertainty in climate change scenario, level of management and crop model used. These conclusions are highly dependent on the specific thresholds of phenology and photosynthesis to change in temperature used in the models. Caution is needed in using the impact assessment results made with the average simulated grain yields and mean changes in climatic parameters.     

The Effect of Spatial Scale of Climatic Change Scenarios on Simulated Maize, Winter Wheat, and Rice Production in the Southeastern United States

We use the CERES family of crop models to assess the effect of different spatial scales of climate change scenarios on the simulated yield changes of maize (Zea mays L.), winter wheat (Triticum aestivum L.),and rice (Oryza sativa L.) in the Southeastern United States. The climate change scenarios were produced with the control and doubled CO₂ runs of a high resolution regional climate model anda coarse resolution general circulation model, which provided the initial and lateral boundary conditions for the regional model. Three different cases were considered for each scenario: climate change alone, climate change plus elevated CO₂, and the latter with adaptations. On the state level,for most cases, significant differences in the climate changed yields for corn were found, the coarse scale scenario usually producing larger modeled yield decreases or smaller increases. For wheat, however, which suffered large decreases in yields for all cases, very little contrast in yield based on scale of scenario was found. Scenario scale resulted in significantly different rice yields, but mainly because of low variability in yields. For maize the primary climate variable that explained the contrast in the yields calculated from the two scenarios is the precipitation during grain fill leading to different water stress levels. Temperature during vernalization explains some contrasts in winter wheat yields. With adaptation, the contrasts in the yields of all crops produced by the scenarios were reduced but not entirely removed. Our results indicate that spatial resolution of climate change scenarios can be an important uncertainty in climate change impact assessments, depending on the crop and management conditions.     

影响我国水稻产量的主要气象因子的研究

通过收集前人所作的全国各地区水稻产量气象预报模型,将所得到的资料按双季早稻、双季晚稻、单季稻进行分析,并提取预报因子。通过定量化处理,使用系统聚类分析方法,以预报方程中的影响因子为指标,讨论了各水稻分区（双季早稻分为４个区,双季晚稻分为２个区,单季稻分为９个区）水稻产量的主、次要影响因子和影响时期,为大范围水稻产量预报提供了科学依据。     

Impact assessment of climate change,carbon dioxide fertilization and constant growing season on rice yields in China

Rice is the staple food in China, and the country’s enlarging population puts increasing pressure on its rice production as well as on that of the world. In this study, we estimate the impact of climate change, CO₂ fertilization, crop adaptation and the interactions of these three factors on the rice yields of China using model simulation with four hypothetical scenarios. According to the results of the model simulation, the rice yields without CO₂ fertilization are predicted to decrease by 3.3 % in the 2040s. Considering a constant rice-growing season (GS), the rice yields are predicted to increase by 3.2 %. When the effect of CO₂ fertilization is integrated into the Agro-C model, the expected rice yields increase by 20.9 %. When constant GS and CO₂ fertilization are both integrated into the model, the predicted rice yield increases by 28.6 %. In summary, the rice yields in China are predicted to decrease in the 2040s by 0.22 t/ha due to climate change, to increase by 0.44 t/ha due to a constant GS and to increase by 1.65 t/ha due to CO₂ fertilization. The benefits of crop adaptation would completely offset the negative impact of climate change. In the future, the most of the positive effects of climate change are expected to occur in northeastern and northwestern China, and the expansion of rice cultivation in northeastern China should further enhance the stability of rice production in China.     

Assessing the impact of climate changes on the potential yields of maize and paddy rice in Northeast China by 2050

Northeast China is the main crop production region in China, and future climate change will directly impact crop potential yields, so exploring crop potential yields under future climate scenarios in Northeast China is extremely critical for ensuring future food security. Here, this study projected the climate changes using 12 general circulation models (GCMs) under two moderate Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and 6.0) from 2015 to 2050. Then, based on the Global Agro-ecological Zones (GAEZ) model, we explored the effect of climate change on the potential yields of maize and paddy rice in Northeast China during 2015–2050. The annual relative humidity increased almost throughout the Northeast China under two RCPs. The annual precipitation increased more than 400 mm in some west, east, and south areas under RCP 4.5, but decreased slightly in some areas under RCP 6.0. The annual wind speed increased over 2 m/s in the west region. The annual net solar radiation changes varied significantly with latitude, but the changes of annual maximum temperature and minimum temperature were closely related to the terrain. Under RCP 4.5, the average maize potential yield increased by 34.31% under the influence of climate changes from 2015 to 2050. The average rice potential yield increased by 16.82% from 2015 to 2050. Under RCP 6.0, the average maize and rice potential yields increased by 25.65% and 6.34% respectively. The changes of maize potential yields were positively correlated with the changes of precipitation, wind speed, and net solar radiation (the correlation coefficients were > 0.2), and negatively correlated with the changes of relative humidity, minimum and maximum temperature under two RCPs. The changes of rice potential yields were positively correlated with the changes of precipitation (correlation coefficient = 0.15) under RCP 4.5. Under RCP 6.0, it had a slight positive correlation with net solar radiation, relative humidity, and wind speed.     

Climate change impacts on regional rice production in China

Rice (Oryza sativa L.) production is an important contributor to China’s food security. Climate change, and its impact on rice production, presents challenges in meeting China’s future rice production requirements. In this study, we conducted a comprehensive analysis of how rice yield responds to climate change under different scenarios and assessed the associated simulation uncertainties of various regional-scale climate models. Simulation was performed based on a regional calibrated crop model (CERES-Rice) and spatially matched climatic (from 17 global climate models), soil, management, and cultivar parameters. Grain-filling periods for early rice were shortened by 2–7 days in three time slices (2030s, 2050s, and 2070s), whereas grain-filling periods for late rice were shortened by 10–19 days in three time slices. Most of the negative effects of climate change were predicted to affect single-crop rice in central China. Average yields of single-crop rice treated with CO₂ fertiliser in central China were predicted to be reduced by 10, 11, and 11% during the 2030s, 2050s, and 2070s, respectively, compared to the 2000s, if planting dates remained unchanged. If planting dates were optimised, single-crop rice yields were predicted to increase by 3, 7, and 11% during the 2030s, 2050s, and 2070s, respectively. In response to climate changes, early and single-crop rice should be planted earlier, and late rice planting should be delayed. The predicted net effect would be to prolong the grain-filling period and optimise rice yield.     

中国七大地区“气候变化—作物产量—经济影响”综合评价

农业作为响应气候变化最敏感的领域之一,未来作物产量可能受到深刻影响。量化气候变化冲击作物产量导致的最终经济影响,需要综合“气候变化—作物产量—经济影响”开展链式研究。文中采用系统回顾和Meta回归分析方法整合了55篇文献的667项研究结果,推导出我国七大地区主要作物（水稻、玉米、小麦）产量与地区内未来温度和降水变化的定量关系,并将其作为农业部门的损失量代入改进的多区域投入产出模型,量化七大地区内与地区间遭受的经济波及影响（ERE)。结果显示：(1)气候变化对我国作物产量的影响主要体现在温度升高上,每升温1℃减产2.6%~12.7%,东北和西北地区作物受升温影响最显著;(2) 气候变化导致的作物减产将对经济产生更严重的波及影响,GDP因作物减产每下降1%将额外产生17.8%的波及影响;(3) 21世纪末,若不考虑CO₂肥效作用,作物减产导致的ERE将占GDP的-0.1%~13.6%（负值表示收益),最悲观情况下ERE与当前我国农业总产值相当（2012年为基准年);(4)不同地区受ERE影响程度的差异较大,因各区之间产业结构、贸易联系及经济发展程度存在差异,西南地区遭受本区及来自其他地区的ERE比华东地区高2.8~8.5倍。     

Assessing the impacts of climate change on rice yields in the main rice areas of China

This paper assesses the impact of climate change on irrigated rice yield using B2 climate change scenario from the Regional Climate Model (RCM) and CERES-rice model during 2071--2090. Eight typical rice stations ranging in latitude, longitude, and elevation that are located in the main rice ecological zones of China are selected for impact assessment. First, Crop Estimation through Resource and Environment Synthesis (CERES)-rice model is validated using farm experiment data in selected stations. The simulated results represent satisfactorily the trend of flowering duration and yields. The deviation of simulation within ± 10% of observed flowering duration and ± 15% of observed yield. Second, the errors of the outputs of RCM due to the difference of topography between station point and grid point is corrected. The corrected output of the RCM used for simulating rice flowering duration and yield is more reliable than the not corrected. Without CO₂ direct effect on crop, the results from the assessment explore that B2 climate change scenario would have a negative impact on rice yield at most rice stations and have little impacts at Fuzhou and Kunming. To find the change of inter-annual rice yield, a preliminary assessment is made based on comparative cumulative probability at low and high yield and the coefficient variable of yield between the B2 scenario and baseline. Without the CO₂ direct effect on rice yield, the result indicates that frequency for low yield would increase and it reverses for high yield, and the variance for rice yield would increase. It is concluded that high frequency at low yield and high variances of rice yield could pose a threat to rice yield at most selected stations in the main rice areas of China. With the CO₂ direct effect on rice yield, rice yield increase in all selected stations.